



from typing import Union

import fire
import torch
from tqdm import tqdm







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		str = "cpu"		,						lowerCAmelCase__:		Union[str, None] = None  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]							      =     torch.load(lowerCAmelCase__		,						map_location=lowerCAmelCase__  )
			for k, v in tqdm(state_dict.items()  ):
						if not isinstance(lowerCAmelCase__		,						torch.Tensor  ):
									raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
						UpperCAmelCase_:		Dict							      =     v.half()
			if save_path is None:  # overwrite src_path
						UpperCAmelCase_:		Tuple							      =     src_path
			torch.save(lowerCAmelCase__		,						lowerCAmelCase__  )


if __name__ == "__main__":
	fire.Fire(convert)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






